The high sensitivity and resolution of liquid chromatography-mass spectrometry (LC-MS) make it an ideal tool for comprehensive analysis of complex biological samples. Comparing spectra obtained from samples corresponding to different patient cohorts (e.g., diseased versus non-diseased, or drug responders versus non-responders) or subjected to different stimuli (e.g., drug administration regimens) can yield valuable information about sample components correlated with particular conditions. Such components may serve as biological markers that enable earlier and more precise diagnosis, patient stratification, or prediction of clinical outcomes. They may also guide the discovery of suitable and novel drug targets. Because this approach extracts a large amount of information from a very small sample size, automated data collection and analysis methods are desirable.
LC-MS data are reported as intensity or abundance of ions of varying mass-to-charge ratio (m/z) at varying chromatographic retention times. A two-dimensional spectrum of LC-MS data from a single sample is shown in FIG. 1, in which the darkness of points corresponds to signal intensity. A horizontal slice of the spectrum yields a mass chromatogram, the abundance of ions in a particular m/z range as a function of retention time. A vertical slice is a mass spectrum, a plot of abundance of ions of varying m/z at a particular retention time interval. The two-dimensional data are acquired by performing a mass scan at regular intervals of retention time. Summing the mass spectrum at each retention time yields a total ion chromatogram (TIC), the abundance of all ions as a function of retention time. Local maxima in intensity (with respect to both retention time and m/z) are referred to as peaks. In general, peaks may span several retention time scan intervals and m/z values.
One significant obstacle for automated analysis of LC-MS data is the nonlinear variability of chromatographic retention times, which can exceed the width of peaks along the retention time axis substantially. This variability arises from, for example, changes in column chemistry over time, instrument drift, interactions among sample components, protein modifications, and minor changes in mobile phase composition. While constant time offsets can be corrected for easily, nonlinear variations are more problematic and significantly hamper the recognition of corresponding peaks across sample spectra. This problem is illustrated by the chromatograms of FIG. 2, in which the dotted and solid curves represent total ion chromatograms of samples from two different patients. While it can be assumed that the dotted curve has been time-shifted from the solid curve, it is difficult to predict from the two curves to which of the two solid peaks the dotted peak corresponds.
Various methods have been provided in the art for addressing the problem of chromatographic retention time shifts, including correlation, curve fitting, and dynamic programming methods such as dynamic time warping and correlation optimized warping. For example, a time warping algorithm is applied to gas chromatography/Fourier transform infrared (FT-IR)/mass spectrometry data from a gasoline sample in C. P. Wang and T. L. Isenhour, “Time-warping algorithm applied to chromatographic peak matching gas chromatography/Fourier transform infrared/mass spectrometry,” Anal. Chem. 59: 649–654, 1987. In this method, a single FT-IR interferogram is aligned with a TIC. While this method may be effective for simple samples, it may be inadequate for more complex samples such as biological fluids, which can contain thousands of different proteins and peptides, yielding thousands of potentially relevant and, more importantly, densely spaced (in both m/z and retention time) peaks.
There is still a need, therefore, for a robust method for time-aligning chromatographic-mass spectrometric data.